Tensor completion via adaptive sampling of tensor fibers: Application to efficient indoor RF fingerprinting
Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang, Min-You Wu
Abstract
In this paper, we consider tensor completion under adaptive sampling of tensor (a multidimensional array) fibers. Tensor fibers or tubes are vectors obtained by fixing all but one index of the array. This sampling is in contrast to the cases considered so far where one performs an adaptive element-wise sampling. In this context we exploit a recently proposed algebraic framework to model tensor data [1] and model the underlying data as a tensor with low tensor tubal-rank. Under this model we then present an algorithm for adaptive sampling and recovery, which is shown to be nearly optimal in terms of sampling complexity. We apply this algorithm for robust estimation of RF fingerprints for accurate indoor localization. We show the performance on real and synthetic data sets. Compared to existing methods, that are primarily based on non-adaptive matrix completion methods, adaptive tensor completion achieves significantly better performance.
BibTeX
@inproceedings{icassp2016_tensorcompletion,
title = {Tensor completion via adaptive sampling of tensor fibers: Application to efficient indoor RF fingerprinting},
author = {Xiao-Yang Liu and Shuchin Aeron and Vaneet Aggarwal and Xiaodong Wang and Min-You Wu},
booktitle = {ICASSP 2016},
year = {2016}
}